Category: Biml

I’m pleased to say that I am speaking at IT/Dev Connections 2017. This year the conference will be held in San Francisco October 23-26. I had a great experience speaking at IT/Dev Connections in 2015, so I am excited to return again this year.

This conference is special to me because of its focus on providing great content for developers and IT pros – the conference website describes it as the “anti-keynote” conference with no forced marketing content.

I also enjoy it because it is more than just SQL Server/Data Platform (they have tracks for Cloud & Data Center, Enterprise Collaboration, Development & Dev Ops, and Enterprise Mobility and Security), and it’s nice to get out of my comfort zone a bit.

If you have been wanting to get into Azure Data Factory (ADF) development, join me for this demo-filled overview. In this session, we’ll go over the basic anatomy of an ADF solution. You’ll learn what ADF is and isn’t as we walk through a solution to pull data from an on-premises SQL Server database to a blob storage and then populate and Azure SQL Data Warehouse. You’ll learn tips for creating ADF solutions in Visual Studio, and I’ll show you how to make ADF development less tedious with a free Visual Studio Add-in called Biml Express. You’ll leave with a basic understanding of ADF and a list of tools and skills you’ll want to acquire as you begin your ADF development.

Improve Data Warehouse ETL Delivery with a Patterns-Based Approach

What if I told you that 90% of your data integration development in SQL Server could be automated? In 5 years, you will be “old fashioned” if you are hand coding SSIS packages. Developers with different skill levels and design preferences create databases and SSIS packages however they see fit to get the job done. Documentation is frequently omitted. Maintenance and small enhancements consume too much development time while manual errors and inconsistencies slip through the testing and release process. You can use tools and frameworks to rearrange the development process and alleviate these common problems. The implementation and automation of design patterns leads to improved efficiency and communication. Join me in this session to learn how to use Business Intelligence Markup Language (Biml) and Excel to facilitate metadata-driven SSIS development. I’ll use database schema information plus Excel inputs to implement a small data mart from staging through the dimensional model.

I showed in my previous post how we generated the datasets for our Azure Data Factory pipelines. In this post, I’ll show the BimlScript for our pipelines. Pipelines define the activities, identify the input and output datasets for those activities, and set an execution schedule. We were creating several pipelines with copy activities to copy data to Azure Data Lake Store.

We generated one pipeline per schedule and load type:

Hourly – Full

Hourly – Incremental

Daily – Full

Daily – Incremental

We also generated some one-time load pipelines for DR/new environment setup.

The first code file below is the template for the pipeline. You can see code nuggets for the data we receive from the generator file and for conditional logic we implemented. The result is one copy activity per source table within the appropriate pipeline.

In the second code file below, lines 104 to 119 are generating the pipelines. We read in the necessary data from the Excel file:

Schema name

Table name

Columns list

Incremental predicate

Sidenote: We wrote a quick T-SQL statement (not shown) to generate the columns list. This could have been done in our BimlScript, but it was something we changed after the fact to accommodate the limitations of Polybase (Dear Microsoft: Please fix). SQL was quicker and easier for us, but if I were to do this again I would add that into our BimlScript. We needed to replace new lines and double quotes in our data before we could read it in from the data lake. You can get around this issue by using .ORC files rather than text delimited files. But the ORC files aren’t human readable, and we felt that was important for adoption of the data lake with the client on this project. They were already jumping in with several new technologies and we didn’t want to add anything else to the stack. So our select statements list out fields and replace the unwanted characters in the string fields.

Our Excel file looks like this.

Columns B, C, L, and M are populated by Excel formulas. This is the file that is read in by the BimlScript in the code below.

In our generator file (which is the same file that was used to generate the datasets), we use the CallBimlScript function to call the pipeline template file and pass along the required properties (table, schema, frequency, scope, columns list, predicate).

The great thing about Biml is that I can use it as much or as little as I feel is helpful. That T-SQL statement to get column lists could have been Biml, but it didn’t have to be. The client can maintain and enhance these pipelines with or without Biml as they see fit. There is no vendor lock-in here. Just as with Biml-generated SSIS projects, there is no difference between a hand-written ADF solution and a Biml-generated ADF solution, other than the Biml-generated solution is probably more consistent.

And have I mentioned the time savings? There is a reason why Varigence gives out shirts that say “It’s Monday and I’m done for the week.”

Apologies for the overly acronym-laden title as I was trying to keep it concise but descriptive. And we all know that adding technologies to your repertoire means adding more acronyms.

My coworker Levi and I are working on a project where we copy data from an on-premises SQL Server 2014 database and land it in Azure Data Lake Store. Then we use Polybase to get the data into Azure SQL Data Warehouse and build a dimensional model. I’ve done a couple of small projects before with Azure Data Factory, but nothing as large as this one. We had 173 tables that we needed to copy to ADLS. Then we needed to set up incremental loads for 95 of those tables going forward.

My Azure Data Factory is made up of the following components:

Gateway – Allows ADF to retrieve data from an on premises data source

Linked Services – define the connection string and other connection properties for each source and destination

Datasets – Define a pointer to the data you want to process, sometimes defining the schema of the input and output data

Pipelines – combine the data sets and activities and define an execution schedule

Each of these objects is defined in a JSON file. Defining data sets and copy activities in JSON gets very tedious, especially when you need to do this for 100+ tables. Tedium usually indicates a repeatable pattern. If there is a repeatable pattern you can probably automate it. The gateway and linked services are one-time setup activities that weren’t worth automating for this project, but the datasets and pipelines definitely were.

In order to automate the generation of datasets and pipelines, we need a little help with some metadata. We had the client help us fill out an Excel spreadsheet that listed each table in our source database and the following characteristics relevant to the load to Azure:

Frequency (daily or hourly)

Changes Only (incremental or full load)

Changed Time Column (datetime column used for incremental loads)

That list plus the metadata we retrieved from SQL server for each table (column names and data types) were all we needed to automate the creation of the ADF datasets and pipelines with BimlScript.

This post will show how we built the data sets. The following post will show the pipelines with the copy activities.

First we need to generate the input datasets coming from SQL Server. We added some properties at the top and embedded some code nuggets to handle the values that are specific to each table.

Next we need the output datasets for Azure Data Lake Store. We use the same three properties in generating each dataset- schema, table, frequency- and we add one more for scope.

Now we just need another BimlScript file that calls these two files. We broke our pipelines up into daily versus hourly and incremental versus full loads.

We used a helper code file and a separate environments file, which I’m glossing over so we can focus on the Biml for the ADF assets. You can see that we read in the inputs from Excel and write some counts to a log file, just to make sure everything is working as intended. Starting on line 41 is where we generate the datasets. On lines 54 and 55, we use the CallBimlScript function to call the two files above. We end up generating datasets for the tables that are a full load each day and their counterpart datasets for the files we create in ADLS. The datasets for daily incremental loads are generated on lines 69 and 70. Then we do the hourly full loads and hourly incremental loads. I’ll discuss lines 100 – 119 in my next post.

The Results

We were able to write the BimlScript and generate the datasets and pipelines in about 35 hours. A previous ADF project without automation took about 3 hours per source table. If we had gone that route, we could have been looking at 350 – 500 hours to complete this part of the project. Visual Studio with Biml Express took about 5 minutes to generate everything. Deploying to Azure took about an hour. We are now looking into ARM templates for future deployments.

Stay tuned for part 2 where I show how we generated the ADF pipelines.

This is post #2 of my BimlScript – Get to Know Your Code Nuggets series. To learn about text nuggets, see my first post.

The next type of BimlScript code nugget I’d like to discuss is the control nugget. Control nuggets allow you to insert control logic to determine what Biml is generated and used to create your BI artifacts (packages, cubes, etc.). Control nuggets can be used to:

Add conditional logic so that specified Biml fragments are only generated when certain criteria are met

Add loops to repeat Biml fragments multiple times

Define variables that will be used later

Access external data from databases, flat files or other sources that will be used in the generation of the final Biml code

Control nuggets start with <# and end with #>. Just like text nuggets, control nuggets can be a single line or multiple lines. And they can contain simple or complex logic.

There were actually control nuggets in the text nugget example from the previous post. The variable declarations at the top of the file (lines 4 – 10) are control nuggets.

Below is another example file that uses code nuggets. I like to separate my design patterns. project connections, and package generation into separate files. The BimlScript shown below is from a slightly altered “caller file”, the file that I would execute to create the SSIS packages that should follow a specified design pattern. This caller file is generating Type 1 slowly changing dimensions.

In my Biml framework, I store the data I need in order to generate my packages in SQL tables. You can see the control nugget in lines 14 – 40 retrieving data from my database and storing it in a data table for later use.

On line 46, there is a control nugget containing a for each loop. For each row in the data table, it calls a BimlScript file that creates a package that uses my Type 1 SCD design pattern with the variables from data table. Notice that the end curly brace for my loop is in a separate control nugget.

There are many different ways to use control nuggets that aren’t covered here. From these two examples you can start to see how I might combine text nuggets and control nuggets to automate my SSIS package creation and employ consistent design patterns. But there are still a few missing pieces that will be filled in when I cover the remaining code nugget types.

In BimlScript, we embed nuggets of C# or VB code into our Biml (XML) in order to replace variables and automate the creation of our BI artifacts (databases, tables, SSIS packages, SSAS cubes, etc.). Code nuggets are a major ingredient in the magic sauce that is meta-data driven SSIS development using BimlScript.

There are 5 different types of code nuggets in BimlScript:

Text nuggets

Control nuggets

Class nuggets

Comments

Directives

Over the next several posts I’ll cover each type of code nugget and provide examples of their use.

Text Nuggets

Text nuggets evaluate the expression they contain and then replace the text nugget with the string representation of the value of the expression. I use them often to switch out names of packages, tasks, and components as well as source and destination tables in SSIS development when creating packages based upon a design pattern.

Text nuggets start with <#= and end with #>. Notice there is an equals sign at the beginning of the text nugget but not at the end.

Text nuggets are very useful. You can include complex business logic in the expressions. And the expression result can be any data type. The BimlScript compiler will automatically convert it to text before replacing the code nugget with the result. Like all code nuggets, text nuggets can be a single line or multiple lines.

Let’s take a look at some examples.

Note: The Biml script below has variables, connections, and a package all in one file for ease of demonstration. Normally I would split these out into separate files.

This BimlScript creates an SSIS package that truncates and reloads data in a single table.

Notice that you can use the text nuggets alone, together, or with literal text.

The <#=PackageName#> text nugget simply replaces the value for the name of the package with the current value of the PackageName variable I set at the top of the file.

The DataFlow Task name is a combination of text and a code nugget.

<Dataflow Name="DFT Stage_<#=DestinationTable#>">

This comes in handy if you would like to enforce certain naming standards. My personal preference is to use naming conventions suggested by Jamie Thomson, so I build them into my Biml and then use text nuggets as shown above to change the rest of the name to reflect the nature of the current task.

The truncate table statement in the Execute SQL Task contains text and two code nuggets that reference a variable and use a function to convert the variable value into all uppercase text:

Hopefully you can see how text nuggets are useful when creating multiple packages at the same time. They allow you to switch out the values that change for each package and create consistent naming standards.

I’m expanding my experiences to speak at different SQL Saturdays this year, and I’m very excited to say that I will be speaking at SQLSaturday Boston on March 19th and SQLSaturday Maine on June 4th.

My session at both SQL Saturdays will focus on using BimlScript to create good ETL patterns. SSIS has been around for a while now, but I see people making the same mistakes that are easily resolved. One of the biggest mistakes is simple inconsistency. BimlScript supports consistency, and forces the developer to (when used correctly) recognize patterns and reuse them. This helps us to solve (and share the solution to) known problems, make it easier for junior developers to solve those problems and learn why our solution works, and move on to new and more interesting challenges. And if we find a flaw in our pattern, it’s a quick update to the Biml pattern and regeneration of packages to make the change to all applicable packages.

Everyone involved in creating and supporting your SSIS projects can appreciate consistent design patterns. It makes your patterns more tested (by reuse in multiple situations) and reduces the learning curve for development and support. BimlScript plus a good ETL framework facilitates quicker start and completion of SSIS projects. As a consultant, I appreciate the consistency when I take over a project (usually, I’m working on phase 2 when someone else built phase 1) and already know how it works. And if the framework is slightly different from what I’m used to (there are valid reasons to build things differently on occasion), I can look at the Biml from the previous work and easily read what’s going on. Have you ever tried to read the XML code of an SSIS package? It’s ugly. Biml is much easier to read because it doesn’t include position information for the UI and many other attributes that aren’t helpful. And because the code is all on one page, I have less chance of missing a setting that was hiding in the advanced properties somewhere in an existing package.

If you are an SSIS architect and you aren’t familiar with Biml, I urge you to look into it. BimlScript.com has a great learning plan to help you get started and become comfortable using Bimlscript. In my opinion, it is currently the best (and in many cases free!) way to create your SSIS framework and design patterns.

In my previous post, I provided the design pattern and Biml for a pure Type 2 Slowly Changing Dimension (SCD). When I say “pure Type 2 SCD”, I mean an ETL process that adds a new row for a change in any field in the dimension and never updates a dimension attribute without creating a new row. In practice, I tend to create more hybrid Type 2 SCDs where updates to some attributes require a new row and others update the value on the existing rows. A similar pattern that I find I implement more often than a pure Type 2 is a Type 6 SCD. A Type 6 SCD builds on the Type 2 technique by adding current attributes alongside the historical attributes so related measures can be grouped by the historical or current dimension attribute values. The only difference between what I call a hybrid Type 2 and a Type 6 is that in the Type 6, there are no Type 1 attributes in the dimension that do not also have a Type 2 version in the dimension to capture the historical values.

Design Pattern

Control Flow

If you are comfortable with my design pattern for a pure Type 2 SCD in which a change of value in any column causes a new row, this pattern is quite similar. And the control flow is exactly the same. This pattern, as with my pure Type 2, assumes that rows are not deleted from the source system. You could easily modify this to check for deleted rows if needed.

Control Flow for a Hybrid Type 2 or Type 6 Dimension

The steps in the control flow are:

Log package start.

Truncate the update table.

Execute the data flow.

Execute the update statements to update columns and insert new rows.

Log package end.

The update statements are different in this pattern, and I’ll explain those in detail below.

Data Flow

The data flow looks like a pure Type 2 SCD, with the exception of an added derived column transformation and minor changes to the lookup and conditional split. Again, I use views to integrate the data, apply business logic, and add hashkeys for change detection. Then I use SSIS to perform the mechanics of loading the data.

The steps in this data flow task are:

Retrieve data from my source view.

Count the rows for package logging purposes.

Perform a lookup to see if the entity already exists in the dimension table.

If the entity doesn’t exist at all in the dimension table, it goes into the left path where I count the number of rows, add a derived column that sets the row start date to “01/01/1900 00:00:00”, and then insert the row into the dimension table.

If the entity does exist in the table, I check it for changes.

If there are changes to the entity, I count the number of rows, us a derived column to flag the type(s) of changes to make, and then insert the row into an update table.

Entities with no changes are simply counted for audit purposes.

The Source View

This SSIS pattern requires 3 hashed values for for change detection:

HistoricalHashKey: the unique identifier of the entity, the natural key that ties the historical rows together

ChangeHashKey: the columns on the dimension that cause a new row to be created and the current row to be expired

UpdateHashKey: the columns on the dimension that should be updated in place

In my example view below, the Route ID and Warehouse identify a unique route. The supervisor, route description and route type are all important attributes of the route. The route area identifies the metro area in which a route is located. If this should change, there is no need to see previous values; we just want to see the current value on every row.

The RowEndDate value in this view will be used for routes that require a current row to be expired since my pattern is the leave the end date of the current row null.

The Change Detection Lookup

The lookup in my DFT compares the HistoricalHashKeyASCII column from the source view with the varchar version of the HistoricalHashKey from the dimension table and adds two lookup columns: lkp_ChangeHashKeyASCII and lkp_UpdateHashKeyASCII to the data set.

Rows that do not match are new rows; i.e., that route has never been in the dimension table before. Rows that do match have a row in the dimension table and will then be evaluated to see if there are any changes in the values for that route.

Derived Column for New Rows

The no match output of the lookup are new rows for routes that are not in the dimension table. Since this is the first row in the table for that route, we want this row to be effective from the beginning of time until the end of time. The beginning of time in this data mart is 01/01/1900. Since the data is loaded multiple times per day, I use a date/time field rather than a date. If you only need the precision of a day, you can cut your row end date/time back to just a date. In my pattern, the current row has a null row end date, but you could easily add a derived column to set the end date to 12/31/9999 if you prefer.

Conditional Split for Change Detection

This time, I have to check to see if both the ChangeHashKeyASCII and the UpdateHashKeyASCII match in my conditional split.

If both hashed values from the source view match the hashed values from the dimension table, then no changes are required and the row is simply counted for audit purposes.

If either hashed value doesn’t match, there is an update to be made.

Derived Column to Identify Change Types

We again compare the UpdateHashKeyASCII value from the source view with that of the dimension. If they don’t match, we set the UpdateInPlace flag to true. If the ChangeHashKeyASCII values don’t match, we set the UpdateNewRow flag to true. If a row has both types of changes, both types of updates will be made.

My update table contains the UpdateInPlace and UpdateNewRow columns, so I can reference these flags in my update statements.

The Update Statements

The update statements in the control flow take the changes from the update table and apply them to the dimension table. Three statements are executed in the Execute SQL Statement labeled SQL Update DimRoute.

The first statement updates the values for the columns designated to be updated in place by joining the update table to the dimension table based on the HistoricalHashKey column. This is the same as performing updates in a Type 1 SCD.

The second statement expires all the rows for which a new row will be added. The third statement inserts the new rows with the RowIsCurrent value set to 1 and the RowEndDate set to null.

The Biml

If you are using Biml, you know that you can create a design pattern for this type of dimension load and reuse it across multiple projects. This speeds up development and ensures that your Type 2 Hybrid or Type 6 dimensions are implemented consistently.

As usual, I have 3 Biml files that are used to create the SSIS package:

ProjectConnections.biml – contains all the project-level connections for the project

Dim2Hybrid.biml – contains the SSIS design pattern with code nuggets that parameterize it to make it reusable

CreateDim2HybridPackages.biml – calls Dim2Hybrid.biml and passes along the values to be used for each package

Once I have my source view, dimension table, and update table in the database, the 3 Biml files added to my project, and BIDSHelper installed, all I have to do is right click on the CreateDim2Hybrid.Biml file and choose Generate SSIS packages to create my package.